In the last few years, machine learning, and deep learning in particular, has demonstrated remarkable performances in a wide variety of fields such as natural language processing [1]}, image recognition [2]} or robotics [3]}. This progress is mainly due to the rise in accessible computing power and the use of neural networks (NN) that act as quasi-universal function approximators that can be optimised fairly easily. This explains the flourishing literature investigating the potential of such techniques in fluid mechanics, as highlighted by [4]}, [5]}.
